Real-time Estimation of Elastic Properties of Formation Rocks Based on Drilling Data by Using an Artificial Neural Network

Abstract

Understanding the mechanical properties of formations has its importance in the drilling, production, and management phases of reservoirs. In petroleum engineering, measurements through wire-line logs, which run in the boreholes after the drilling phase, are of the most common methods to estimate mechanical properties of different layers. Elastic rock mechanic measurements from logs are dynamic values and need to be upscaled and calibrated to fit the corresponding pseudo-static measurements that are obtained from cores in the laboratory. As a practical approach to having a continuous profile of these static parameters and surmount the arduousness that confronts by using core samples and laboratory tests, many researchers tried to deploy predictive methods and empirical correlations. However, it will be a great advantage to have a real-time estimation of these static parameters while drilling based on bit and formation rock interaction. Artificial neural networks are powerful tools for estimation of complex functions subjected to availability of large enough data sets that present samples of actual behavior of the function. In this study, artificial neural networks were implemented to estimate in situ rock mechanical properties, including Unconfined Compressive Strength, Young's Modulus of Elasticity, and the ratio of these parameters known as Modulus Ratio, by using operational drilling parameters as inputs. Required data were gathered from drilling reports, logging operations, and core samples from nine wells placed in Ahwaz Oilfield located in south-west Iran. The trained networks showed satisfactory low errors in the testing process and sketched the capability of artificial neural networks in estimation of complex functions. The accuracy of the presented model was then compared with the results of calibrated regional correlations and modified Warren's equation. These correlations are used in the candidate wells to estimate Unconfined Compressive Strength and Young's Modulus of Elasticity. It was observed that the new artificial neural network approach is a competent and accurate method for real-time calculation of static elastic properties of formation rocks. The results of this work could be used for drilling optimization, reducing stability problems, and lithologic boundary detection while drilling.

title = "Real-time Estimation of Elastic Properties of Formation Rocks Based on Drilling Data by Using an Artificial Neural Network",

abstract = "Understanding the mechanical properties of formations has its importance in the drilling, production, and management phases of reservoirs. In petroleum engineering, measurements through wire-line logs, which run in the boreholes after the drilling phase, are of the most common methods to estimate mechanical properties of different layers. Elastic rock mechanic measurements from logs are dynamic values and need to be upscaled and calibrated to fit the corresponding pseudo-static measurements that are obtained from cores in the laboratory. As a practical approach to having a continuous profile of these static parameters and surmount the arduousness that confronts by using core samples and laboratory tests, many researchers tried to deploy predictive methods and empirical correlations. However, it will be a great advantage to have a real-time estimation of these static parameters while drilling based on bit and formation rock interaction. Artificial neural networks are powerful tools for estimation of complex functions subjected to availability of large enough data sets that present samples of actual behavior of the function. In this study, artificial neural networks were implemented to estimate in situ rock mechanical properties, including Unconfined Compressive Strength, Young's Modulus of Elasticity, and the ratio of these parameters known as Modulus Ratio, by using operational drilling parameters as inputs. Required data were gathered from drilling reports, logging operations, and core samples from nine wells placed in Ahwaz Oilfield located in south-west Iran. The trained networks showed satisfactory low errors in the testing process and sketched the capability of artificial neural networks in estimation of complex functions. The accuracy of the presented model was then compared with the results of calibrated regional correlations and modified Warren's equation. These correlations are used in the candidate wells to estimate Unconfined Compressive Strength and Young's Modulus of Elasticity. It was observed that the new artificial neural network approach is a competent and accurate method for real-time calculation of static elastic properties of formation rocks. The results of this work could be used for drilling optimization, reducing stability problems, and lithologic boundary detection while drilling.",

T1 - Real-time Estimation of Elastic Properties of Formation Rocks Based on Drilling Data by Using an Artificial Neural Network

AU - Jamshidi, E.

AU - Arabjamaloei, R.

AU - Hashemi, A.

AU - Ekramzadeh, M. A.

AU - Amani, M.

PY - 2013/2/15

Y1 - 2013/2/15

N2 - Understanding the mechanical properties of formations has its importance in the drilling, production, and management phases of reservoirs. In petroleum engineering, measurements through wire-line logs, which run in the boreholes after the drilling phase, are of the most common methods to estimate mechanical properties of different layers. Elastic rock mechanic measurements from logs are dynamic values and need to be upscaled and calibrated to fit the corresponding pseudo-static measurements that are obtained from cores in the laboratory. As a practical approach to having a continuous profile of these static parameters and surmount the arduousness that confronts by using core samples and laboratory tests, many researchers tried to deploy predictive methods and empirical correlations. However, it will be a great advantage to have a real-time estimation of these static parameters while drilling based on bit and formation rock interaction. Artificial neural networks are powerful tools for estimation of complex functions subjected to availability of large enough data sets that present samples of actual behavior of the function. In this study, artificial neural networks were implemented to estimate in situ rock mechanical properties, including Unconfined Compressive Strength, Young's Modulus of Elasticity, and the ratio of these parameters known as Modulus Ratio, by using operational drilling parameters as inputs. Required data were gathered from drilling reports, logging operations, and core samples from nine wells placed in Ahwaz Oilfield located in south-west Iran. The trained networks showed satisfactory low errors in the testing process and sketched the capability of artificial neural networks in estimation of complex functions. The accuracy of the presented model was then compared with the results of calibrated regional correlations and modified Warren's equation. These correlations are used in the candidate wells to estimate Unconfined Compressive Strength and Young's Modulus of Elasticity. It was observed that the new artificial neural network approach is a competent and accurate method for real-time calculation of static elastic properties of formation rocks. The results of this work could be used for drilling optimization, reducing stability problems, and lithologic boundary detection while drilling.

AB - Understanding the mechanical properties of formations has its importance in the drilling, production, and management phases of reservoirs. In petroleum engineering, measurements through wire-line logs, which run in the boreholes after the drilling phase, are of the most common methods to estimate mechanical properties of different layers. Elastic rock mechanic measurements from logs are dynamic values and need to be upscaled and calibrated to fit the corresponding pseudo-static measurements that are obtained from cores in the laboratory. As a practical approach to having a continuous profile of these static parameters and surmount the arduousness that confronts by using core samples and laboratory tests, many researchers tried to deploy predictive methods and empirical correlations. However, it will be a great advantage to have a real-time estimation of these static parameters while drilling based on bit and formation rock interaction. Artificial neural networks are powerful tools for estimation of complex functions subjected to availability of large enough data sets that present samples of actual behavior of the function. In this study, artificial neural networks were implemented to estimate in situ rock mechanical properties, including Unconfined Compressive Strength, Young's Modulus of Elasticity, and the ratio of these parameters known as Modulus Ratio, by using operational drilling parameters as inputs. Required data were gathered from drilling reports, logging operations, and core samples from nine wells placed in Ahwaz Oilfield located in south-west Iran. The trained networks showed satisfactory low errors in the testing process and sketched the capability of artificial neural networks in estimation of complex functions. The accuracy of the presented model was then compared with the results of calibrated regional correlations and modified Warren's equation. These correlations are used in the candidate wells to estimate Unconfined Compressive Strength and Young's Modulus of Elasticity. It was observed that the new artificial neural network approach is a competent and accurate method for real-time calculation of static elastic properties of formation rocks. The results of this work could be used for drilling optimization, reducing stability problems, and lithologic boundary detection while drilling.